gmvarkit (version 1.4.1)

update_numtols: Update the stationarity and positive definiteness numerical tolerances of an existing class 'gmvar' model.

Description

update_numtols updates the stationarity and positive definiteness numerical tolerances of an existing class 'gmvar' model.

Usage

update_numtols(gmvar, stat_tol = 0.001, posdef_tol = 1e-08)

Arguments

gmvar

an object of class 'gmvar' created with fitGMVAR or GMVAR.

stat_tol

numerical tolerance for stationarity of the AR parameters: if the "bold A" matrix of any regime has eigenvalues larger that 1 - stat_tol the model is classified as non-stationary. Note that if the tolerance is too small, numerical evaluation of the log-likelihood might fail and cause error.

posdef_tol

numerical tolerance for positive definiteness of the error term covariance matrices: if the error term covariance matrix of any regime has eigenvalues smaller than this, the model is classified as not satisfying positive definiteness assumption. Note that if the tolerance is too small, numerical evaluation of the log-likelihood might fail and cause error.

Value

Returns an object of class 'gmvar' defining a structural GMVAR model with the modified structural parameters and constraints.

Details

All signs in any column of \(W\) can be swapped without changing the implied reduced form model. Consequently, also the signs in the columns of the B-matrix are swapped. Note that the sign constraints imposed on \(W\) (or the B-matrix) are also swapped in the corresponding columns accordingly.

Also the order of the columns of \(W\) can be changed (without changing the implied reduced form model) as long as the order of lambda parameters is also changed accordingly. This can be done with the function reorder_W_columns.

References

• Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.

• Virolainen S. 2020. Structural Gaussian mixture vector autoregressive model. Unpublished working paper, available as arXiv:2007.04713.

See Also

fitGMVAR, GMVAR, GIRF, reorder_W_columns, gmvar_to_sgmvar

Examples

# NOT RUN {
# Structural GMVAR(2, 2), d=2 model identified with sign-constraints:
params222s <- c(-11.964, 155.024, 11.636, 124.988, 1.314, 0.145, 0.094, 1.292,
 -0.389, -0.07, -0.109, -0.281, 1.248, 0.077, -0.04, 1.266, -0.272, -0.074,
  0.034, -0.313, 0.903, 0.718, -0.324, 2.079, 7.00, 1.44, 0.742)
W_222 <- matrix(c(1, 1, -1, 1), nrow=2, byrow=FALSE)
mod222s <- GMVAR(p=2, M=2, d=2, params=params222s, parametrization="mean",
                 structural_pars=list(W=W_222))
mod222s

# Update numerical tolerances:
mod222s <- update_numtols(mod222s, stat_tol=1e-4, posdef_tol=1e-9)
mod222s
# }